Fig. REF  shows the comparison among a typical top-down method Postnet[1]}, a SOTA bottom-up method SMAP [2]} and our method. We selected four challenging frames from MuPoTS-3D which included extreme occlusion cases and extreme hard poses. As shown in the second row of Fig. REF , we observe that although the top-down method achieves high accuracy with PCK metrics, they suffer from high redundancy for the human detection bounding box and cannot handle well in occlusion case. Specifically, when a person is partially occluded, high redundancy detection bounding box will greatly affect the performance. Besides, SOTA bottom-up method suffers from person scale variation and also cannot handle partial occlusion cases. As shown in the third row of Fig. REF , we observe that they cannot detect the partially occluded person behind as well as cannot accurately regress extreme hard poses. By contrast, our method can produce reasonable relative poses and reasonable depth in the above cases as shown in the fourth row of Fig. REF . We also provide results of the quality results on in-the-wild images from the COCO dataset of val2017 set as shown in Fig. REF . The results indicate that our method is applicable in in-the-wild cases.
